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Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS)

Journal of Exposure Science & Environmental Epidemiology volume 32, pages 808–819 (2022)Cite this article

Abstract

Background

Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus.

Objective

The aim of our study is to develop a computational approach that can be used to evaluate the of extend to which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood.

Methods

We collected experimental values of the concentration ratio between cord and maternal blood (RCM) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We used the compiled database to, train and test an artificial neural network (ANN). And then applied the best performing model to predict RCM for a large dataset of PFAS chemicals (n = 7982). We, finally, examined the calculated physicochemical descriptors of the chemicals to identify which properties correlated significantly with RCM.

Results

We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of RCM for PFAS suggested that 3623 compounds had a log RCM > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane, and nonafluoro-tert-butyl 3-methylbutyrate.

Significance

These observations have important public health implications as many PFAS have been shown to interfere with fetal development. In addition, as these compounds are highly persistent and many of them can readily cross the placenta, they are expected to remain in the population for a long time as they are being passed from parent to offspring.

Impact

Understanding the behavior of chemicals in the human body during pregnancy is critical in preventing harmful exposures during critical periods of development. Many chemicals can cross the placenta and expose the fetus, however, the mechanism by which this transport occurs is not well understood. In our study, we developed a machine learning model that describes the transplacental transfer of chemicals as a function of their physicochemical properties. The model was then used to make predictions for a set of 7982 per- and polyfluorinated alkyl substances that are listed on EPA’s CompTox Chemicals Dashboard. The model can be applied to make predictions for other chemical categories of interest, such as plasticizers and pesticides. Accurate predictions of RCM can help scientists and regulators to prioritize chemicals that have the potential to cause harm by exposing the fetus.

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Fig. 1: Workflow for compiling the RCM database and for training and testing the models.
Fig. 2: Log non-lipid adjusted RCM of the chemicals in the compiled database presented by chemical category and sorted by median RCM from the highest to the lowest.
Fig. 3: Cross validation using a shuffle-split method (n = 100 times) for dividing the data set into training and testing with an 80:20 split.
Fig. 4: Y-randomization analysis following a cross-validation using a shuffle-split method (n = 100 times) for dividing the data set into training and testing sets with an 80:20 split.
Fig. 5: Predictions of log RCM for the 23 PFAS from the maternal-cord database when the 23 compounds were included in the training set and when they were included in the testing test.
Fig. 6: Model predictions for PFAS in the PFASMASTER list.
Fig. 7: Applicability R2 (AR2) calculated for the chemicals in the RCM database and in PFASMASTER.
Fig. 8: Examples of RDKit bits that showed a significant association with RCM.
Fig. 9: Examples of PFAS that are expected to cross the placenta and preferentially partition to cord blood (RCM > 0).

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Data availability

The manuscript is accompanied by a supporting information document file and 3 supplementary spreadsheets with the all the underlying data (Supplementary Spreadsheet 1 – Database, Supplementary Spreadsheet 2 – Grid Search, Supplementary Spreadsheet 3 – Modeling Results). All code, Supplementary Documents and spreadsheets are available on GitHub (https://github.com/dimitriabrahamsson/pfas-maternal-cord).

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Acknowledgements

We want to thank Nisha Sipes (U.S. EPA) Ian Cousins (Stockholm University), and Matthew MacLeod (Stockholm University) for their thoughtful comments and immensely valuable suggestions.

Funding

This study was funded by the Office of Environmental Health Hazard Assessment (OEHHA) of the California Environmental Protection Agency (CalEPA) and by the National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS) (grant numbers K99ES032892, P30-ES030284, P01ES022841, R01ES027051). The views expressed in this manuscript are those of the authors and do not necessarily represent the views and policies of OEHHA.

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Authors and Affiliations

  1. Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA

    Dimitri Abrahamsson, Adi Siddharth, Joshua F. Robinson & Tracey J. Woodruff

  2. California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA

    Anatoly Soshilov, Sarah Elmore, Vincent Cogliano, Elaine Khan & Lauren Zeise

  3. California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA

    Anatoly Soshilov, Sarah Elmore, Vincent Cogliano, Elaine Khan & Lauren Zeise

  4. Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara St, Pittsburgh, PA, 15261, USA

    Carla Ng

  5. Wisconsin Institute for Discovery, University of Wisconsin, Madison, 330 N Orchard St, Madison, WI, 53715, USA

    Randolph Ashton

  6. The Stem Cell and Regenerative Medicine Center, University of Wisconsin, Madison, 1111 Highland Avenue, Madison, WI, 53705, USA

    Randolph Ashton

  7. Department of Biomedical Engineering, University of Wisconsin - Madison, 1550 Engineering Drive, Madison, WI, 53706, USA

    Randolph Ashton

  8. Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA

    Weihsueh A. Chiu

  9. Department of Obstetrics, Gynecology, and Reproductive Science and the Center of Reproductive Science, University of California, San Francisco, San Francisco, CA, 94143-2240, USA

    Jennifer Fung

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Contributions

DA collected the data, conducted the analyses and wrote the manuscript. A Si assisted in the data collection and analysis. JFR, JF, and TJW supervised, provided feedback and assisted with the writing of the manuscript. A So, SE, VC, EK, and LZ provided feedback on the analyses and assisted with the writing of the manuscript. CN, RA, WC constituted the advisory panel of the study, provided feedback and assisted with the analyses and the writing of the manuscript.

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Correspondence to Dimitri Abrahamsson or Tracey J. Woodruff.

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Abrahamsson, D., Siddharth, A., Robinson, J.F. et al. Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS). J Expo Sci Environ Epidemiol 32, 808–819 (2022). https://doi.org/10.1038/s41370-022-00481-2

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  • DOI: https://doi.org/10.1038/s41370-022-00481-2

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